animal density cell and as expected, all the other
teams attacked that cell. Similar results were obtained
for group G7, which placed a coverage of 0.50 on the
highest animal density target. Other teams performed reasonably well but none performed better
than maximin, which would have resulted in a
defender expected utility of – 2. 17.

Comparing the university students’ results with
those of the high school students (figure 3), visual
inspection suggests that overall, the high school students outperformed the university students. This
may be due to the limited time allotted to the university students. The high school students had more
time, which allowed more discussion and scrutinizing over decisions. However, the small sample sizes
prohibited statistical comparison, so this observation
should be interpreted with caution.

Feedback
A total of 24 ( 7 males and 17 females) out of 30 university students responded to a survey that assessed
their experiences in the unit. Questions mirrored
those administered to the high school students to
address unit objectives. More than 69 percent of
respondents indicated that the activity increased
their interest in AI at least somewhat, and more than
80 percent agreed (somewhat or more) that the activity was a valuable learning experience. Additionally,
more than 65 percent responded that they would recommend the activity to peers. Qualitative data suggested that respondents particularly enjoyed the
interactive aspects of the unit. The least enjoyable
aspects of the unit were cases in which students in a
team couldn’t agree on a particular strategy.

Security ExpertsA three-day workshop was developed in collabora-tion with the World Wildlife Fund (WWF) to demon-strate the value of AI-based solutions for security tosecurity experts who protect wildlife. The workshopwas held in Bandar Lampung, Sumatra, Indonesia, inMay 2015. A game theory–based decision aid calledPAWS (Yang et al. [2014]; Protection Assistant forWildlife Security) was developed, in part, based on astudy of green security games for the purpose of pro-tecting wildlife from poaching. We sought to teachhow AI systems like PAWS fed with partial informa-tion can generate patrol strategies that can performrelative to strategies created by field experts withextensive knowledge of the system. Hence, diverginga bit from the classroom-based units described above,our objectives for this third audience included pro-moting participant adoption of AI-based software(that is, PAWS), sharpening participants’ probabilisticreasoning skills especially in the poacher role, andparticipant satisfaction with the learning experience.

Participants

A total of 28 participants ( 26 males and 2 females)
attended the workshop. They represented the five primary groups (either government or NGO) involved
in protecting wildlife in Bukit Barisan Selatan and
Tesso Nilo national parks on Sumatra: the Indonesian
National Park Service, WWF, Wildlife Conservation
Society, Indonesian Rhino Foundation, and prosecution officers from the court. The majority of these
individuals were rangers with a great deal of domain
expertise in wildlife crime and protection who directly conduct field patrols over conservation areas; the
prosecutors report cases to lawyers and judges who
can open official investigations to prosecute wildlife
crime. The mean age of the sample was 35.0 years (SD
= 7. 5), and mean years of formal schooling was 14.0
(SD = 3. 1). Approximately 60 percent of respondents
identified their job sector as wildlife/national park
protection, 20 percent as nonprofit/NGO, and 20 percent as law enforcement, and overall they had an
average of 9. 6 years of experience working in wildlife
protection (SD = 6. 1).

Participants were native speakers of Bahasa
Indonesia. The instructors delivered the workshop in
English and interpreters translated all the material
between instructors and participants throughout the
three-day course. All written materials were made
available in both English and Bahasa Indonesia.

Unit Structure
We began by introducing basic examples and theoretical foundations relevant to agent-based modeling,
game theory, and security games through lectures.
Building on and integrating this knowledge as part of
our scaffolding framework, we next presented applications that leverage multiple AI techniques. Learners
also discussed in groups various challenges faced in
wildlife protection and solutions for those challenges, including AI-based solutions. They played the
computer-based game as poachers. On the last day,
they had the opportunity to integrate and apply their
knowledge in playing the board game as poachers
and rangers. They also reflected on their results and
shared ideas for improving patrolling effectiveness.
These interactive exercises provided learners with a
new lens for understanding poachers’ behaviors and
limits of manual patrolling strategies, as well as introducing the methodology and advantages of game-theoretic solutions.

Security Game Tutorials
On the first day of the workshop, we introduced security game examples from several domains, beginning
with a basic security game. We explained how the
defender could optimally conduct patrols over targets
and how attackers may respond against that strategy.
We next covered ( 1) real-world applications of security games for protecting critical infrastructure and
( 2) challenges in wildlife protection and the applica-